Abstract: This follow-up study examines the accuracy of selected
smartphone sound measurement applications (apps) using external
calibrated microphones. The initial study examined 192 apps on the iOS
and Android platforms and found four iOS apps with mean differences
of 62 dB of a reference sound level measurement system. This study
evaluated the same four apps using external microphones. The results
showed measurements within 61 dB of the reference. This study sug-
gests that using external calibrated microphones greatly improves the
overall accuracy and precision of smartphone sound measurements,
and removes much of the variability and limitations associated with the
built-in smartphone microphones.
[CC]
Date Received: July 27, 2016 Date Accepted: September 20, 2016

1. Introduction
The National Institute for Occupational Safety and Health (NIOSH) estimates that
more than 22 million people in the United States are exposed to noise levels in excess
of 85 A-weighted decibels (dBA) at their place of work. The World Health
Organization (WHO) estimates that more than 5% of the work population—
360 million people—have disabling hearing loss (WHO, 2012). Occupational noise-
induced hearing loss is preventable; however, once acquired, it is permanent and irre-
versible (NIOSH, 1998). Understanding and minimizing the risks associated with noise
exposures are the keys to preventing noise-related hearing loss. The availability of
sound measurement apps can serve to raise people’s awareness about their work (and
off-work) environment and allows them to make informed decisions about the poten-
tially hazardous effects of noise on their hearing and well-being. The ubiquity of smart-
phones, their constant network connectivity, the built-in geographic information system
functionality, and user-interactivity features present distinct advantages over uncon-
nected and often bulky and expensive professional sound level measurement instru-
ments. Smartphone features provide users and researchers an opportunity to revolu-
tionize the way noise data are collected and shared.
Professional sound level meters (SLMs) must comply with national and inter-
national standards such as American National Standards Institute (ANSI) S1.4-1983
(R2007), Specifications for Sound Level Meters (ANSI, 1983) and International
Electrotechnical Commission (IEC) 61672-1 (IEC, 2013). Both standards specify a host
of acoustical and electrical tests with indicated tolerance limits and measurement
uncertainties that are specified in decibels over a wide frequency range (typically from
10 Hz–20 kHz). Such tests must account for level linearity, directionality, time and
frequency-weighting responses, tonebursts, radio frequency interference, and atmo-
spheric and environmental conditions. The standards also specify that these tests shall
be made on the complete instrument, including the microphone and pre-amplifier. As
of today, no smartphone or smartphone-based app has met the requirements of IEC or
ANSI standards. For our studies, and because of the challenges associated with sub-
jecting smartphones to the full spectrum of tests required by national and international
standards, we used one testing aspect from the ANSI S1.4 standard that states, “the
expected total allowable error for a sound level meter measuring steady broadband
noise in a reverberant sound field is approximately 61.5 dB for a type 1 instrument
and 62.3 dB for a type 2 instrument.” We recognize that this only tests one of the
requirements specified in SLM standards and we want to emphasize that smartphones
and smartphone sound apps were not designed to meet such rigorous standards

(operate within tolerance limits set in those standards) since their main intended pur-
pose was as communication devices rather than sound level measurement devices.
In 2014, we examined 192 sound measurement apps on the iOS and
Android platforms and found only 4 iOS apps that had the means of their differ-
ences with a type 1 SLM to be within 62 dB over a 65–95 dB sound pressure level
(SPL) test range. Overall, none of the Android-based apps met our initial test crite-
ria, mainly because the Android marketplace is fragmented among many manufac-
turers with different requirements for parts and lack of uniform audio integration of
software and hardware across the different devices (Kardous and Shaw, 2014). The
digital circuitry and computational capabilities of a smartphone far exceed the
power, speed, and storage capability of any professional SLM on the market today.
However, a major weakness remains the micro-electro-mechanical-system (MEMS)
built-in microphone used in smartphones. Advances in MEMS microphone design
and technology show that these microphones now rival the best electret and con-
denser microphones used in current sound measurement instruments in terms of
frequency response, power requirements, and environmental/electromagnetic specifi-
cations. MEMS microphones continue to have certain limitations because of their
miniature size and circuit board placement, which affect their dynamic range and
signal-to-noise ratio response (Robinson and Tingay, 2014). Another major con-
straint presented by the built-in microphones is the lack of access and inability to
perform periodic or pre-measurement calibration. Several apps have a feature that
allows users to attach an external microphone to the iOS devices headset jack input.
Few “audio measurement” external microphones are available commercially that
use the four contact, Tip-Ring1-Ring2-Sleeve (TRRS) configuration for use with
most smartphone headset jacks. Two external microphones with similar specifica-
tions were selected for this study, an inexpensive Dayton Audio iMM-6
(Springboro, OH) microphone and the more expensive MicW i436 (Beijing, China)
that is reported by the manufacturer to be in compliance with IEC 61672-1 Class 2
specification. Both microphones use electret-condenser capsules and are omnidirec-
tional. The main consideration for selecting the two microphones was their wide
availability commercially and their size (ability to fit into a typical acoustical cali-
brator adapter). Table 1 provides an overview of the main characteristics of the
microphones.
This paper describes a follow-up study that examined the performance and
accuracy of the four smartphone iOS apps from the original study when used with two
different external calibrated microphones.
2. Methods
For this study, we used the same experimental setup as in the first study to conduct
our testing–we generated pink noise with a 20 Hz–20 kHz frequency range, at levels
from 65 to 95 dB in 5-dB increments (7 different noise levels). We examined the accu-
racy of the unweighted (or flat) sound levels for each device over the 65–95 dB SPL
test range. The measurement range was chosen to reflect the majority of typical occu-
pational noise exposures encountered in the workplace today. The measurements were
conducted in a diffuse sound field at a reverberant chamber at the NIOSH Acoustic
Testing Laboratory. The diffuse sound field ensured that the location, orientation, and
size of the microphones did not influence the results of the study. Noise generation and
acquisition were performed using the Trident software (ViAcoustics, Austin, TX).
Noise was generated through three JBL XRX715 two-way loudspeakers oriented to
provide maximum sound diffusivity inside the chamber. Reference sound level meas-
urements were obtained using a 1/2-inch Larson-Davis (DePew, NY) model 2559 ran-
dom incidence microphone. In addition, a Larson-Davis model 831 type 1 SLM was
used as a secondary reference, mostly for confirmation of the laboratory-based system
and verification of the overall results. Both the reference system and the SLM are con-
sidered to be type 1/class 1 devices as indicated in ANSI S1.4 and IEC-61672-1 stand-
ards. The microphone and SLM were calibrated before and after each measurement
using the Larson-Davis model CAL250 precision acoustic calibrator. All the reference

measurement instrumentation used in this study underwent annual calibration at a
National Institute of Standards and Technology accredited laboratory. Smartphones
were set up on a stand in the middle of the chamber at a height of approximately 4 ft
to mimic the height of a person conducting a smartphone-based noise measurement.
Figure 1 shows the test setup inside the reverberant chamber and arrangements of the
speakers and smartphones.
The experiment was conducted using a split plot design with nominal sound
level as the whole plot factor and app as the split-plot factor. The study was conducted
using 4 apps (SoundMeter, SPLnFFT, SPL Pro, and NoiSee), 7 nominal sound levels
(65, 70, 75, 80, 85, 90, and 95 dB), and 6 blocks. A total of 6 different iPhones (3
iPhone 5S’s and 3 iPhone 6’s) and 6 different sets of iMM-6 and i436 external micro-
phones were used. Each block consisted of a unique iPhone with a unique external
microphone. The experimental design was such that the difference (in dB SPL) between
the outputs of the reference system and the apps was measured for all sound levels and
all apps in each block. Two experiments were conducted, one for a set of i436 micro-
phones and another for a set of iMM-6 microphones. Each smartphone/microphone
combination was calibrated separately before and after each measurement at 94 dB
using a Larson-Davis CAL 150B acoustic calibrator.
To analyze the data, we generated a randomization sampling schedule and
employed analysis of variance using both SAS (Cary, SC) and Stata software (College
Station, TX). We used the difference between the actual sound level (as measured by
the reference system) and the app measurement as the outcome variable, and then deter-
mined the effects of apps and sound levels on this outcome. A difference equal to zero
would indicate perfect agreement between the app measurement and the actual value.
The larger the difference, the poorer the agreement between the app and the reference
system.
3. Results
The results of testing the fixed effects of the smartphone apps showed that there was
no evidence of differences between apps, both for the iMM-6 [p ¼ 0.5614, F ¼ 0.69,
df ¼ (3,105)] and for the i436 [p ¼ 0.5382, F ¼ 0.73, df ¼ (3,105)] microphones. Also,
there was no evidence that the measurements of the four apps differed from those
made by the reference system; the least squares means of differences did not differ sig-
nificantly from zero, as indicated by the fact that all of the 95% confidence intervals
for the estimates contained zero (see Table 2). In testing the fixed effects of nominal

sound levels, there were two main findings: (1) there was no evidence that the mea-
sured differences depended on the nominal sound level for both the iMM-6
[p ¼ 0.9852, F ¼ 0.16, df ¼ (6,30)] and for the i436 [p ¼ 0.3593, F ¼ 1.15, df ¼ (6,30)]
microphones, and (2) there was no evidence of an interaction between nominal sound
levels and apps. Tukey-adjusted multiple comparisons of the apps were performed, and
as expected, there were no differences overall due to “app.”
Figure 2 shows box plots of the differences between the reference system and
the app measurements for the four apps (SoundMeter, SPLnFFT, SPL Pro, and
NoiSee) over the seven nominal sound levels for both the iMM-6 and i436 external
microphones.
The results show that the differences in measurements between the reference
system and each of the SoundMeter and SPL Pro apps were mostly between 61 dB for
all sound levels for both the iMM-6 and i436 microphones while the SPLnFFT and
NoiSee apps appeared to have slightly wider variations for the iMM-6 microphones at
the 65–75 dB sound levels.
Figure 3 shows box plots of the differences between the reference system and
app measurements by app and by nominal sound level for both the iMM-6 and i436
microphones. Visual inspection of the graphs suggests that the medians of the differ-
ences for the iMM-6 microphones were slightly higher than those for the i436
microphones.

Fig. 3. (Color online) (a) Box plots of the differences between the reference and app measurements for both
iMM-6 and i436 microphones by app (top), and (b) by nominal sound levels (bottom).

Figure 4 shows box plots of the differences between the reference system and
app measurements by app and by sound level for the internal versus the external micro-
phones. Data from the internal microphones were gathered from the previous study.
The results show that the use of external and calibrated microphones improved the
accuracy and precision of the measurements, the mean difference obtained using the

Fig. 4. (Color online) (a) Box plots of differences between the reference and app measurements for internal and
external microphones by app (top), and (b) by nominal sound levels. Data for the internal microphones were
gathered in our previous study (Kardous and Shaw, 2014) (bottom).

external microphones, 0.023 6 0.530 [mean 6 standard deviation], was considerably
less than that obtained for internal microphones, 1.646 6 3.795, as was the range for
external microphones, (1.4, 1.8) [min, max], compared to that for the internal micro-
phones, (14, 11.3).

4. Discussion
The manufacturer, MicW, claims that the i436 microphone complies with IEC 61672
class 2 SLM standard. It is important to note that IEC-61672 provides specifications
for SLMs as an entire system (microphone, signal processor, and a display device)
whether it is a self-contained, hand-held instrument, or a combination of the above,
not just the microphones.
The MicW i436 microphone has an outer metal housing that is uniform in
size and fits perfectly into a 1/4 in. acoustical calibrator adapter. The Dayton-Audio
iMM-6 microphones have a plastic housing and are not as ruggedly constructed; they
also had very slight differences in the housing size that presented some problems in fit-
ting the microphone into the calibrator adapter. It is possible that those fitting issues
during calibration contributed to the slight underperformance of the iMM-6 with the
SPLnFFT and NoiSee apps at lower sound levels.
Overall, all four apps performed well using both sets of external microphones.
It is interesting to note that the medians of the differences for the iMM-6 microphones
are slightly higher than those measurements made with the i436 microphones. This
means that the measurements taken using the iMM-6 microphones tend to be slightly
lower (0.1–0.2 dB) than those made with the i436 microphones. This is possibly due to
differences in the frequency responses and the nominal sensitivities of both microphones.
As seen in Fig. 4, the use of external, calibrated, microphones improved the
accuracy and precision of noise measurements compared with the previous study
(Kardous and Shaw, 2014), when we evaluated sound measurement apps using the
smartphones’ built-in microphones. This improvement in accuracy and precision indi-
cates that the microphone is the primary reason for the wide variations in measure-
ments, not the app or other smartphone circuitry or hardware. Although issues such as
construction and “class 2 compliance” are important considerations in the selection of
an external microphone, such considerations must be balanced against the tenfold price
difference between the two microphones. For users interested in exploring the use of
smartphones for performing professional or occupational noise measurements using
smartphones, it is imperative that an appropriate external calibrated microphone is
selected and used in conjunction with any smartphones app to achieve an acceptable
level of accuracy (Roberts et al., 2016). Since the publication of the original study, the
iOS ecosystem has grown drastically, new applications have been introduced, and older
applications have been refined and improved. The results of the study suggests that
additional apps, especially the 10 that met our initial selection criteria could perform
better (over the same testing range, pink noise from 65 to 95 dB SPL) when used with
an external calibrated microphone.
Since the acquisition of acoustical calibrators may be prohibitively expensive
for some users, some app developers have implemented pre-defined profiles for external
microphones by incorporating known sensitivity values that the user can select, and
the app calculations will be adjusted accordingly based on those sensitivity values. As
more external microphones become available commercially, we expect developers to
start including those pre-defined microphone profiles into their apps or make them
available for uploading on their sites. Although pre-defined profiles may solve the need
for calibration on a short term basis, microphone performance could degrade over
time, especially if dropped or repeatedly exposed to extreme environmental conditions.
Professional instruments are typically calibrated before and after every measurement
and are also sent out for calibration at accredited laboratories. Because this practice
may not be feasible with smartphones, it should not be assumed that pre-defined pro-
files will continue to work with a specific microphone over a long period of time.
Routine checks with an acoustic calibrator before and after each measurement session
will remain the preferred method for obtaining accurate readings.
Although not examined in this study, the use of external calibrated micro-
phones may lead to similar findings when used with Android-based apps. One of the
main issues encountered with Android-based apps in the earlier study was the frag-
mented marketplace for hardware devices and lack of uniformity of audio integration
between the tens of different manufacturers. The selection and use of an external, cali-
brated microphone removes many such obstacles.

As with the earlier study, this follow-up study has several limitations and con-
straints—mainly testing the performance and accuracy over a limited range of sound
levels and not testing for level linearity, directionality, time and frequency-weighting
responses, tonebursts, radio frequency interference, and atmospheric and environmen-
tal conditions as specified in IEC 61672 standard for SLMs. Other issues such as pri-
vacy, extended data collection, battery life, as well as data storage and sharing con-
tinue to present many challenges to the rate of adoption of apps for use in lieu of
professional sound measurement instruments. Since Apple moved away from the cur-
rent TRRS plugs for headsets, we expect microphone manufacturers to adapt but
could impact current pricing and availability.
5. Conclusions
This study expands our previous study that evaluated the performance of sound mea-
surement apps to examine the performance of such apps using external calibrated
microphones. The study showed that the use of external calibrated microphones greatly
enhances the accuracy and precision of smartphone-based noise measurements.
Overall, there appeared to be no substantial difference in the type of microphone
selected as long as it was appropriately calibrated, preferably by using an acoustical
calibrator instead of relying on the pre-defined profiles available from some developers.
Although the study is limited in scope, and smartphone apps are still unlikely to
replace professional instruments or comply with applicable ANSI or IEC standards in
the near future, the results of this study indicate that, due to the advancements made
in app design and external microphones availability, the gap between professional
instruments and smartphone-based apps is rapidly narrowing.
Acknowledgments
The findings and conclusions in this study are those of the authors and do not necessarily
represent the views of NIOSH. Mention of any company or product does not constitute
endorsement by NIOSH.
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